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Online 1. Towards a deeper understanding of molecular mechanics [2018]
 Hernández, Carlos Xavier, author.
 [Stanford, California] : [Stanford University], 2018.
 Description
 Book — 1 online resource.
 Summary

The advent of atomistic molecular dynamics simulations held the promise of a complete understanding of biomolecular dynamics. However, this goal has remained elusive, as increased computational power has brought with it larger systems to simulate and an overwhelming number of observables to analyze. In this work, I describe how recent advancements in Markov state modeling have helped overcome this dimensionality problem and enabled the characterization of complex phenomena, such as the foldinguponbinding processes of intrinsically disordered peptides. But is it possible to produce even more insightful models? To this end, I present a method that exploits Markov state models to infer statistically causal drivers of protein dynamics. Finally, I discuss a neural network alternative to Markov models, which yields physically interpretable insights and has the potential to replace expensive atomistic simulations.
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3781 2018 H  Unavailable In process 
 Harrigan, Matthew P.
 2017.
 Description
 Book — 1 online resource.
 Summary

Biology is the ultimate emergent phenomenon, and we largely lack a full picture of its function at the smallest scales. Molecular dynamics purports to model biomolecules like proteins with allatom resolution. Among other challenges, merely analyzing the large quantities of data that result from a simulation has become a bottleneck. In this dissertation, I present my work towards building reducedcomplexity models that faithfully capture the relevant functional dynamics of biomolecular simulations. In chapter 1, I introduce a mathematical language for dealing with stochastic processes and show the connection to established modeling methods like Markov modeling and tICA. Chapter 2 develops and characterizes a method for including solvent degrees of freedom in Markov state models. In chapter 3, we apply stateoftheart MSM modeling to understand multiscale conformational dynamics of a potassium ion channel. Chapter 4 provides an overview of a curated selection of modeling building blocks accessible through our carefully designed software package. Chapter 5 introduces a new nonlinear basis which unites the MSM and tICA approaches. Finally, in chapter 6, I introduce parameterized sets of basis functions and use the variational principle directly to optimize the basis set itself. It is my hope that these novel algorithms aided by wellengineered software implementations and validated by characterization on real biomolecular systems will lead the field closer towards truly robust dynamical models of biomolecules.
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3781 2017 H  Inlibrary use 
 McGibbon, Robert T.
 2016.
 Description
 Book — 1 online resource.
 Summary

Understanding the conformational dynamics of biological macromolecules at atomic resolution remains a grand challenge at the intersection of biology, chemistry, and physics. Molecular dynamics (MD)  which refers to computational simulations of the atomiclevel interactions and equations of motions that give rise to these dynamics  is a powerful approach that now produces immense quantities of time series data on the dynamics of these systems. Here, I describe a variety of new methodologies for analyzing the rare events in these MD data sets in an automatic, staticallysound manner, and constructing the appropriate simplified models of these processes. These techniques are rooted in the theory of reversible Markov chains. They include new classes of Markov state models, hidden Markov models, and reaction coordinate finding algorithms, with applications to protein folding and conformational change. A particular focus herein is on methods for model selection and model comparison, and computationally efficient algorithms.
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3781 2016 M  Inlibrary use 
Online 4. Automated construction of order parameters for analyzing simulations of protein folding and water dynamics [electronic resource] [2015]
 Schwantes, Christian R.
 2015.
 Description
 Book — 1 online resource.
 Summary

This thesis discusses the role of analysis in the application of molecular dynamics (MD) simulations for studying phenomena at an atomic level of detail. Many interesting questions can be asked of the physical world: How do proteins fold? Are there two liquid phases of supercooled water? How does a drug inhibit an enzyme? Many of these questions, however, are not quantitatively specific, meaning that the answers depend on scientists' interpretation. Researchers tend to describe these phenomena through handchosen functions, or order parameters, that are based on physical intuition. However, there is an immense amount of information contained in a largescale simulation that is typically not used. Here, we attempt to illustrate the usefulness of datadriven analysis in the study of physical phenomena with MD. In all cases, the strategy begins by reformulating the nebulous physical questions into a specific and quantitative question, which can be used to derive an appropriate unsupervised learning method for the given problem. This strategy rests on physical intuition, because the problem formulation must be done in a physically meaningful way, but the advantage is that the specific solutions are driven by the data itself, allowing for interesting phenomena to be discovered rather than required to be known a priori.
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Online 5. Modern theory of protein folding [electronic resource] [2015]
 Lane, Thomas Joseph.
 2015.
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 Book — 1 online resource.
 Summary

Despite over half a century of study, many interesting questions concerning protein folding remain. I detail the gaps in our theoretical understanding of folding, describe how new theories and experiments could fill those gaps, and discuss my own attempts to understand one of biology's most amazing phenomena. Specifically, I discuss how master equations can be used to understand folding kinetics, how we can understand the surprising simplicity of folding kinetics, and how a experiments determining the ratelength law of folding could contribute to our ability to falsify theoretical arguments. I conclude with five stimulating questions that if answered would further our understanding of folding significantly.
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3781 2015 L  Inlibrary use 
 Weber, Jeffrey Kurt.
 2014.
 Description
 Book — 1 online resource.
 Summary

The diverse physical principles that govern living things conform to one common precept: all biological processes operate, to some extent, outof equilibrium. As our understanding of biological pathways advances at the nanoscale, theoretical and simulation techniques that function under nonequilibrium conditions will play an important role in elucidating the working environment of the cell. In this research, largescale molecular dynamics simulations, discrete dynamical network models, and sophisticated nonequilibrium theories are synthesized to study glassy and dissipative processes facilitated by protein molecules. Leveraging atomistic molecular dynamics data derived from the Folding@home distributed computing project, a number of detailed biophysical systems are examined. I first describe glassy solvent structures that emerge as functional components of a protein chaperone, and I connect such observations to the theory of disordered systems. By coupling Folding@home data, Markov state models of biomolecular dynamics, and the theory of large deviations, I go on to characterize [beta] sheetrich, amyloidlike misfolded states that appear on protein folding landscapes; I explore the relationship between these misfolded states and socalled dynamical glass transitions. Applying theory related to the Crooks fluctuation theorem, I next explicate the dissipative dynamics in detailed models of signaling proteins, and I illustrate how the input of external energy harmonizes with equilibrium fluctuations to yield functional signaling components. Lastly, I discuss methods by which protein landscapes can be sampled in an adaptive fashion and means for recovering equilibrium kinetics from biased, nonequilibrium simulation data.
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3781 2014 W  Inlibrary use 
Online 7. Enabling ab initio molecular dynamics for large biological molecules [electronic resource] [2011]
 Ufimtsev, Ivan.
 2011.
 Description
 Book — 1 online resource.
 Summary

The role of atomistic modeling of molecules and organic compounds in biology and pharmaceutical research is constantly increasing, providing insights on chemical and biological phenomena at the highest resolution. To achieve relevant results, however, computational biology has to deal with systems containing at least 1000 atoms. Such big molecules cause large computational demands and impose limitations on the level of theory used to describe molecular interactions. Classical molecular mechanics based on various empirical relationships has become a workhorse of computational biology, as a practical compromise between accuracy and computational cost. Several decades of classical force field development have seen many successes. Nevertheless, more accurate treatment of biomolecules from first principles is highly desirable. HartreeFock (HF) and density functional theory (DFT) are two lowlevel ab initio methods that provide sufficient accuracy to interpret experimental data. They are therefore the methods of choice to study large biological systems. Recently DFT has been applied to calculate single point energy of a solvated Rubredoxin protein. The system contained 2825 atoms and required more than two hours on a supercomputer with 8196 parallel cores. This study clearly demonstrates the scale of problems one has to tackle in first principles calculations of biologically relevant systems. Dynamical simulations requiring thousands of single point energy and force evaluations therefore appear to be completely out of reach. This fact has essentially prohibited the use of first principles methods for many important biological systems. Fortunately, the computer industry is evolving quickly and novel computing architectures such as graphical processing units (GPUs) are emerging. The GPU is an indispensable part any modern desktop computer. It is special purpose hardware responsible for graphics processing. Most problems in computer graphics are embarrassingly parallel, meaning they can be split into a large number of smaller subproblems that can be solved in parallel. This fact has guided GPU development for more than a decade; and modern GPUs evolved into a massively parallel computing v architecture containing hundreds of basic computational units, which all together can perform trillions of arithmetic operations per second. The large computational performance and low price of consumer graphics cards makes it tempting to consider using them for computationally intensive general purpose computing. This fact was recognized long ago and several groups of enthusiasts attempted to use GPUs for nongraphics computing in the early 2000's. One of the few successes from these attempts is now known as Folding@Home. These early attempts were primarily stymied by three major problems: lack of adequate development frameworks, limited precision available on GPUs, and the difficulty of mapping existing algorithms onto the new architecture. The two former impediments have been recently alleviated by the introduction of efficient GPU programming toolkits such as CUDA and the latest generation of graphics cards supporting full double precision arithmetic operations in hardware. These advances led to an explosion of interest in general purpose GPU computing and led to the development of many GPUbased high performance applications in various fields such as classical molecular dynamics, magnetic resonance imaging, and computational fluid dynamics. Most of the projects, however, lie far outside of quantum chemistry which is likely caused by the complexity of quantum chemistry algorithms and the associated difficulty of mapping them onto the GPU architecture. Various specific features of the hardware require complete redesign of conventional HF and DFT algorithms in order to fully benefit from the large computational performance of GPUs. We have successfully solved this problem and implemented the new algorithms in TeraChem, a high performance general purpose quantum chemistry package designed for graphical processing units from the ground up. Using TeraChem, we performed the first ab initio molecular dynamics simulation of an entire Bovine pancreatic trypsin inhibitor (BPTI) protein for tens of picoseconds on a desktop workstation with eight GPUs operating in parallel. Coincidently, this was also the first protein ever simulated on a computer using the classical molecular mechanics approach. BPTI binds to trypsin with a binding free energy of approximately 20 kcal/mol, making BPTI one of the strongest noncovalent binders. It vi is even more remarkable that a single BPTI amino acid LYS15 contributes half of the binding free energy by forming a salt bridge with one of the trypsin's negatively charged residues inside the binding pocket. In fact, the LYS15's contribution to the overall binding energy is approximately twice as large as what would be expected based on experimental measurements of salt bridge interactions in other proteins. Our simulation of BPTI demonstrated that substantial charge transfer occurs at the proteinwater interface, where between 2.0 and 3.5 electrons are transferred from the interfacial water to the protein. This effect decreases the net protein charge from +6e as observed in gasphase experiments to +4e or less. We demonstrate how this effect may explain the unusual binding affinity of the LYS15 amino acid.
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Online 8. Improving and applying atomistic simulation to study biophysical conformational dynamics [2018]
 McKiernan, Keri A., author.
 [Stanford, California] : [Stanford University], 2018.
 Description
 Book — 1 online resource.
 Summary

Models are tools used to interpret and draw conclusions from nature. Molecular dynamics (MD) simulation is a powerful technique for modeling complex atomistic systems such as biomolecules. In this dissertation, I discuss how one can improve and apply MD simulation in order to learn about biophysical phenomena. I first discuss how to improve the representation of the underlying physical interactions in a simulation. Chapter 2 discusses the optimization method, and 3 discusses how to rigorously characterize a resultant potential function. I then discuss how to use Markov state modeling to derive an interpretable mechanistic characterization of a simulation dataset. Chapters 4 and 5 apply this framework to study the conformational dynamics of the TREK2 and CLC2 ion channels, respectively. A brief introduction to the topics of MD simulation, force field optimization, and Markov state modeling is given in chapter 1. There remains a lot of work to be done before simulations are able to mimic reality with high fidelity. However, I am optimistic that with increasing data availability and improvements in optimization methodology, simulation will prove itself progressively more useful for studying dynamics at atomic resolution.
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3781 2018 M  Inlibrary use 
Online 9. Enabling ab initio molecular dynamics for large biological molecules [2011]
 Ufimtsev, Ivan S.
 Aug. 2011.
 Description
 Book — online resource (xv, 95 pages) : illustrations (some color)
 Summary

The role of atomistic modeling of molecules and organic compounds in biology and pharmaceutical research is constantly increasing, providing insights on chemical and biological phenomena at the highest resolution. To achieve relevant results, however, computational biology has to deal with systems containing at least 1000 atoms. Such big molecules cause large computational demands and impose limitations on the level of theory used to describe molecular interactions. Classical molecular mechanics based on various empirical relationships has become a workhorse of computational biology, as a practical compromise between accuracy and computational cost. Several decades of classical force field development have seen many successes. Nevertheless, more accurate treatment of biomolecules from first principles is highly desirable. HartreeFock (HF) and density functional theory (DFT) are two lowlevel ab initio methods that provide sufficient accuracy to interpret experimental data. They are therefore the methods of choice to study large biological systems. Recently DFT has been applied to calculate single point energy of a solvated Rubredoxin protein. The system contained 2825 atoms and required more than two hours on a supercomputer with 8196 parallel cores. This study clearly demonstrates the scale of problems one has to tackle in first principles calculations of biologically relevant systems. Dynamical simulations requiring thousands of single point energy and force evaluations therefore appear to be completely out of reach. This fact has essentially prohibited the use of first principles methods for many important biological systems. Fortunately, the computer industry is evolving quickly and novel computing architectures such as graphical processing units (GPUs) are emerging. The GPU is an indispensable part any modern desktop computer. It is special purpose hardware responsible for graphics processing. Most problems in computer graphics are embarrassingly parallel, meaning they can be split into a large number of smaller subproblems that can be solved in parallel. This fact has guided GPU development for more than a decade; and modern GPUs evolved into a massively parallel computing v architecture containing hundreds of basic computational units, which all together can perform trillions of arithmetic operations per second. The large computational performance and low price of consumer graphics cards makes it tempting to consider using them for computationally intensive general purpose computing. This fact was recognized long ago and several groups of enthusiasts attempted to use GPUs for nongraphics computing in the early 2000's. One of the few successes from these attempts is now known as Folding@Home. These early attempts were primarily stymied by three major problems: lack of adequate development frameworks, limited precision available on GPUs, and the difficulty of mapping existing algorithms onto the new architecture. The two former impediments have been recently alleviated by the introduction of efficient GPU programming toolkits such as CUDA and the latest generation of graphics cards supporting full double precision arithmetic operations in hardware. These advances led to an explosion of interest in general purpose GPU computing and led to the development of many GPUbased high performance applications in various fields such as classical molecular dynamics, magnetic resonance imaging, and computational fluid dynamics. Most of the projects, however, lie far outside of quantum chemistry which is likely caused by the complexity of quantum chemistry algorithms and the associated difficulty of mapping them onto the GPU architecture. Various specific features of the hardware require complete redesign of conventional HF and DFT algorithms in order to fully benefit from the large computational performance of GPUs. We have successfully solved this problem and implemented the new algorithms in TeraChem, a high performance general purpose quantum chemistry package designed for graphical processing units from the ground up. Using TeraChem, we performed the first ab initio molecular dynamics simulation of an entire Bovine pancreatic trypsin inhibitor (BPTI) protein for tens of picoseconds on a desktop workstation with eight GPUs operating in parallel. Coincidently, this was also the first protein ever simulated on a computer using the classical molecular mechanics approach. BPTI binds to trypsin with a binding free energy of approximately 20 kcal/mol, making BPTI one of the strongest noncovalent binders. It vi is even more remarkable that a single BPTI amino acid LYS15 contributes half of the binding free energy by forming a salt bridge with one of the trypsin's negatively charged residues inside the binding pocket. In fact, the LYS15's contribution to the overall binding energy is approximately twice as large as what would be expected based on experimental measurements of salt bridge interactions in other proteins. Our simulation of BPTI demonstrated that substantial charge transfer occurs at the proteinwater interface, where between 2.0 and 3.5 electrons are transferred from the interfacial water to the protein. This effect decreases the net protein charge from +6e as observed in gasphase experiments to +4e or less. We demonstrate how this effect may explain the unusual binding affinity of the LYS15 amino acid.
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